import pandas as pd
import statsmodels.formula.api as smf  
from sqlalchemy import create_engine
import pymysql
from matplotlib import pyplot as plt
import seaborn as sns

db_config = {
    'host': 'localhost',
    'user': 'root',
    'password': 'sjk1234',
    'database': 'tushare',
    'port': 3306,
    'charset': 'utf8mb4'
}

conn = pymysql.connect(**db_config)
chunk_size = 10000

df = pd.read_sql_query("""
SELECT d.*, m.buy_lg_vol, m.sell_lg_vol, m.buy_elg_vol, m.sell_elg_vol, m.net_mf_vol, i.vol as i_vol, i.closes as i_closes 
FROM date_1 d 
JOIN moneyflows m on d.ts_code = m.ts_code and d.trade_date = m.trade_date 
LEFT JOIN index_daily i on d.trade_date = i.trade_date and i.ts_code = '399001.SZ' 
WHERE d.trade_date BETWEEN '2023-01-01' AND '2023-12-31' and d.ts_code = '002229.SZ'
""",
conn,
chunksize=chunk_size
)
df1 = pd.concat(df, ignore_index=True)
print(df1.head)

df1['zd_closes'] = round((df1['closes'] - df1['closes'].shift(1)) / df1['closes'].shift(1), 2)
df1['zs_closes'] = round((df1['i_closes'] - df1['i_closes'].shift(1)) / df1['i_closes'].shift(1), 2)
df1['zs_vol'] = round((df1['i_vol'] - df1['i_vol'].shift(1)) / df1['i_vol'].shift(1), 2)
print(df1.head)

# 处理缺失值
df1 = df1.dropna(subset=['zd_closes', 'zs_closes', 'zs_vol'])

ex = ['id', 'ts_code', 'trade_date', 'the_date', 'opens', 'high', 'low', 'closes', 'pre_closes', 'changes', 'pct_chg']
number = df1.select_dtypes(include=['number']).columns.to_list()
number_list = [i for i in number if i not in ex]

formula = 'zd_closes ~ ' + ' + '.join(number_list)
res = smf.ols(formula, data=df1).fit()
print(res.summary())

plt.figure(figsize=(10, 6))
sns.scatterplot(x='zd_closes', y='zs_closes', data=df1)
plt.show()

features = df1[['vol', 'amount', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol', 'zs_vol']]
correlation_matrix = features.corr()
print(correlation_matrix)

plt.figure(figsize=(10, 6))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=.5)
plt.title('Correlation Matrix')
plt.show()
